Real Robot Challenge 2021: Cartesian Position Control with Triangle Grasp and Trajectory Interpolation

TL;DR

This paper describes a near-optimal Cartesian position control approach for robotic grasping and trajectory interpolation, emphasizing grasp stability and controller performance, demonstrated in the Real Robot Challenge 2021 with publicly available code.

Contribution

The paper introduces a novel combination of triangular grasp and trajectory interpolation to improve stability and performance in robot goal-reaching tasks.

Findings

Triangular grasp improves stability over pinch grasp.

Trajectory interpolation enhances goal-reaching speed.

Approach achieves near-optimal trajectory performance.

Abstract

We present our runner-up approach for the Real Robot Challenge 2021. We build upon our previous approach used in Real Robot Challenge 2020. To solve the task of sequential goal-reaching we focus on two aspects to achieving near-optimal trajectory: Grasp stability and Controller performance. In the RRC 2021 simulated challenge, our method relied on a hand-designed Pinch grasp combined with Trajectory Interpolation for better stability during the motion for fast goal-reaching. In Stage 1, we observe reverting to a Triangular grasp to provide a more stable grasp when combined with Trajectory Interpolation, possibly due to the sim2real gap. The video demonstration for our approach is available at https://youtu.be/dlOueoaRWrM. The code is publicly available at https://github.com/madan96/benchmark-rrc.